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Smart scanning and image interpretation

Paul Kruegel

Artificial intelligence not only facilitates the generation of medical images. With its help, image data can also be interpreted in new ways – and diagnoses can be made more precise and meaningful.

The idea is visionary, but nevertheless obvious: could artificial intelligence (AI) make diagnostic scans more precise and meaningful – and thus ultimately make therapy more individual and reliable?

Today, AI already supports imaging at various levels, such as recording and processing image data. In the future, however, it will also become increasingly important for image interpretation.

As in other areas, such as speech recognition on smartphones, AI in medicine is often based on artificial neural networks. This refers to computer algorithms that imitate the networking and function of the nerve cells in the brain (even if they are by no means a lifelike image of the cerebral cortex). Such algorithms are capable of learning and can, for example, be trained with the data from computer tomography in such a way that they independently recognise anatomical structures.

Image processing is also considerably facilitated by AI-based anatomical pattern recognition. Thanks to the technology, radiologists can view the right kidney or left acetabulum in seconds, for example, in extensive 3D image data sets, display the correct numbering of ribs and vertebral bodies or precisely compare the images with previous scans.

Siemens Healthineers recently introduced two digital companions – powerful AI-enriched systems called the AI-Pathway Companion1 and the AI-Rad Companion². The latter, being an intelligent services platform for radiologists, may help to reduce the time of interpretation and reporting. It automatically performs measurements and prepares results in the form of valuable clinical images and reports. AI-Rad Companion Chest CT is fully integrated in the image interpretation workflow and helps to handle the daily workload with more ease.

Images become data sets

What is particularly fascinating, however, is that AI algorithms can now also be used by physicians for the actual diagnosis. One example is the automated analysis of skull CTs in order to promptly detect unexpected brain hemorrhages.

Even image information that cannot be seen with the naked eye can be revealed by advanced AI applications. For cancer patients, for example, it is in principle possible to use computerised image data analyses to identify specific patterns that make it possible to better assess the course of the disease or the success of the therapy.

In other words, AI could make medical images even more valuable in the future. This would make them all the more beneficial for individual patient care.

1 The product/feature mentioned herein is under development and not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.2 AI-Rad Companion is 510(k) pending, and not yet commercially available in the United States and other countries.